Physical properties and thermodynamic data are essential inputs to all computer-aided molecular design applications. Basic properties, such as the melting-point temperature, are essential for developing custom chemicals with desired thermophysical behavior. Currently, accurate correlations for the melting-point temperature are limited, including recent attempts to use quantitative structure-property relationships (QSPR). The lack of a comprehensive melting-point model can be attributed to (a) the sensitivity of the melting point to subtle variations in molecular structure and (b) the inability of existing molecular descriptors to account satisfactorily for all factors that influence the melting-point behavior. In this work, we present a new QSPR model for predicting the melting-point temperature of a diverse organic dataset. The model benefits from the inclusion of novel nonlinear descriptors developed through the use of robust genetic algorithms(GAs) and neural networks. Three new descriptors were developed to account for the nonlinear effect of molecular weight, hydrogen bonding, and molecular symmetry on the melting-point temperature. The resultant QSPR model is capable of modeling melting-point behavior with a root-mean-square error (RMSE) of 12.6 K, an average absolute percent deviation (%AAD) of 4.7 in absolute temperature, and a correlation coefficient of R-2 = 0.95. This work differs from other literature efforts in that (a) an extensive dataset of over 1250 molecules is used for model development and (b) descriptor selection is performed using nonlinear algorithms. The results suggest that often-ignored structural descriptors such as molecular weight and symmetry have major roles in determining the melting point.